Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations102465
Missing cells16834
Missing cells (%)0.7%
Duplicate rows630
Duplicate rows (%)0.6%
Total size in memory19.5 MiB
Average record size in memory200.0 B

Variable types

Text3
Numeric17
Categorical4
DateTime1

Alerts

Dataset has 630 (0.6%) duplicate rowsDuplicates
Average_rating is highly overall correlated with Deviation of star ratingsHigh correlation
Deviation of star ratings is highly overall correlated with Average_rating and 2 other fieldsHigh correlation
FOG Index is highly overall correlated with Flesch Reading Ease and 1 other fieldsHigh correlation
Flesch Reading Ease is highly overall correlated with FOG Index and 1 other fieldsHigh correlation
Rating is highly overall correlated with Deviation of star ratings and 1 other fieldsHigh correlation
Topic_4 is highly overall correlated with breadth and 1 other fieldsHigh correlation
breadth is highly overall correlated with Topic_4High correlation
depth is highly overall correlated with Topic_4High correlation
sentiment_score_continuous is highly overall correlated with Deviation of star ratings and 3 other fieldsHigh correlation
sentiment_score_discrete is highly overall correlated with sentiment_score_continuous and 1 other fieldsHigh correlation
sst5_sentiment_score is highly overall correlated with sentiment_score_continuous and 1 other fieldsHigh correlation
text_length is highly overall correlated with FOG Index and 1 other fieldsHigh correlation
has_images is highly imbalanced (76.6%) Imbalance
sentiment_score_continuous has 8328 (8.1%) missing values Missing
sentiment_score_discrete has 8328 (8.1%) missing values Missing
Helpfulness is highly skewed (γ1 = 102.4016524) Skewed
Helpfulness has 92646 (90.4%) zeros Zeros

Reproduction

Analysis started2025-01-16 04:35:16.390492
Analysis finished2025-01-16 04:36:20.105847
Duration1 minute and 3.72 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Distinct77
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size800.6 KiB
2025-01-16T13:36:20.789535image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length199
Median length166
Mean length134.54865
Min length37

Characters and Unicode

Total characters13786527
Distinct characters79
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPanasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.8
2nd rowPanasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.8
3rd rowPanasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.8
4th rowPanasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.8
5th rowPanasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.8
ValueCountFrequency (%)
80768
 
3.6%
with 57899
 
2.6%
for 48762
 
2.2%
to 41523
 
1.9%
and 41342
 
1.9%
tv 32069
 
1.4%
wireless 28068
 
1.3%
black 25463
 
1.1%
ipad 22043
 
1.0%
mount 20670
 
0.9%
Other values (692) 1824455
82.1%
2025-01-16T13:36:21.946808image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2120597
 
15.4%
e 935979
 
6.8%
o 739754
 
5.4%
a 708160
 
5.1%
t 696047
 
5.0%
i 663703
 
4.8%
r 605098
 
4.4%
n 514982
 
3.7%
l 468796
 
3.4%
s 394611
 
2.9%
Other values (69) 5938800
43.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13786527
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2120597
 
15.4%
e 935979
 
6.8%
o 739754
 
5.4%
a 708160
 
5.1%
t 696047
 
5.0%
i 663703
 
4.8%
r 605098
 
4.4%
n 514982
 
3.7%
l 468796
 
3.4%
s 394611
 
2.9%
Other values (69) 5938800
43.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13786527
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2120597
 
15.4%
e 935979
 
6.8%
o 739754
 
5.4%
a 708160
 
5.1%
t 696047
 
5.0%
i 663703
 
4.8%
r 605098
 
4.4%
n 514982
 
3.7%
l 468796
 
3.4%
s 394611
 
2.9%
Other values (69) 5938800
43.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13786527
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2120597
 
15.4%
e 935979
 
6.8%
o 739754
 
5.4%
a 708160
 
5.1%
t 696047
 
5.0%
i 663703
 
4.8%
r 605098
 
4.4%
n 514982
 
3.7%
l 468796
 
3.4%
s 394611
 
2.9%
Other values (69) 5938800
43.1%

Average_rating
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5064959
Minimum3.8
Maximum4.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size800.6 KiB
2025-01-16T13:36:22.129267image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile4
Q14.4
median4.6
Q34.7
95-th percentile4.8
Maximum4.9
Range1.1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.22974209
Coefficient of variation (CV)0.050980207
Kurtosis1.1777493
Mean4.5064959
Median Absolute Deviation (MAD)0.1
Skewness-1.2280697
Sum461758.1
Variance0.052781429
MonotonicityNot monotonic
2025-01-16T13:36:22.507345image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
4.6 29824
29.1%
4.7 23018
22.5%
4.5 13708
13.4%
4.3 11889
 
11.6%
4.4 6440
 
6.3%
4.8 4737
 
4.6%
3.9 3262
 
3.2%
4.1 2888
 
2.8%
4.2 2670
 
2.6%
3.8 1837
 
1.8%
Other values (2) 2192
 
2.1%
ValueCountFrequency (%)
3.8 1837
 
1.8%
3.9 3262
 
3.2%
4 1108
 
1.1%
4.1 2888
 
2.8%
4.2 2670
 
2.6%
4.3 11889
 
11.6%
4.4 6440
 
6.3%
4.5 13708
13.4%
4.6 29824
29.1%
4.7 23018
22.5%
ValueCountFrequency (%)
4.9 1084
 
1.1%
4.8 4737
 
4.6%
4.7 23018
22.5%
4.6 29824
29.1%
4.5 13708
13.4%
4.4 6440
 
6.3%
4.3 11889
 
11.6%
4.2 2670
 
2.6%
4.1 2888
 
2.8%
4 1108
 
1.1%

Num_of_Rating
Real number (ℝ)

Distinct77
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50291.504
Minimum15398
Maximum223181
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size800.6 KiB
2025-01-16T13:36:22.689900image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum15398
5-th percentile16453
Q121988
median36537
Q362436
95-th percentile122681
Maximum223181
Range207783
Interquartile range (IQR)40448

Descriptive statistics

Standard deviation40864.967
Coefficient of variation (CV)0.81256204
Kurtosis5.0991517
Mean50291.504
Median Absolute Deviation (MAD)16550
Skewness2.1172569
Sum5.153119 × 109
Variance1.6699455 × 109
MonotonicityNot monotonic
2025-01-16T13:36:22.884035image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110444 1954
 
1.9%
33336 1934
 
1.9%
76290 1837
 
1.8%
104579 1766
 
1.7%
24205 1652
 
1.6%
33087 1646
 
1.6%
119789 1619
 
1.6%
18908 1601
 
1.6%
201075 1564
 
1.5%
64529 1558
 
1.5%
Other values (67) 85334
83.3%
ValueCountFrequency (%)
15398 1035
1.0%
15469 1084
1.1%
16023 1180
1.2%
16085 1444
1.4%
16453 1108
1.1%
17206 1428
1.4%
17230 1015
1.0%
17318 1084
1.1%
18061 1012
1.0%
18244 1398
1.4%
ValueCountFrequency (%)
223181 1363
1.3%
201075 1564
1.5%
148591 1138
1.1%
122681 1316
1.3%
119789 1619
1.6%
110468 1308
1.3%
110444 1954
1.9%
104579 1766
1.7%
100244 1540
1.5%
85201 1006
1.0%

Rating
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size800.6 KiB
5.0
69648 
1.0
12571 
4.0
9513 
3.0
 
5948
2.0
 
4785

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters307395
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row1.0
3rd row3.0
4th row5.0
5th row1.0

Common Values

ValueCountFrequency (%)
5.0 69648
68.0%
1.0 12571
 
12.3%
4.0 9513
 
9.3%
3.0 5948
 
5.8%
2.0 4785
 
4.7%

Length

2025-01-16T13:36:23.074497image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-16T13:36:23.272378image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
5.0 69648
68.0%
1.0 12571
 
12.3%
4.0 9513
 
9.3%
3.0 5948
 
5.8%
2.0 4785
 
4.7%

Most occurring characters

ValueCountFrequency (%)
. 102465
33.3%
0 102465
33.3%
5 69648
22.7%
1 12571
 
4.1%
4 9513
 
3.1%
3 5948
 
1.9%
2 4785
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 307395
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 102465
33.3%
0 102465
33.3%
5 69648
22.7%
1 12571
 
4.1%
4 9513
 
3.1%
3 5948
 
1.9%
2 4785
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 307395
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 102465
33.3%
0 102465
33.3%
5 69648
22.7%
1 12571
 
4.1%
4 9513
 
3.1%
3 5948
 
1.9%
2 4785
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 307395
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 102465
33.3%
0 102465
33.3%
5 69648
22.7%
1 12571
 
4.1%
4 9513
 
3.1%
3 5948
 
1.9%
2 4785
 
1.6%
Distinct61336
Distinct (%)59.9%
Missing41
Missing (%)< 0.1%
Memory size800.6 KiB
2025-01-16T13:36:24.186686image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length100
Median length87
Mean length21.515328
Min length1

Characters and Unicode

Total characters2203686
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55687 ?
Unique (%)54.4%

Sample

1st rowIts a good product
2nd rownothing came in
3rd rowGreat for basement or garage use
4th rowPROSCONS Good things come in SMALL packages
5th rowDoesnt pick up well
ValueCountFrequency (%)
great 18199
 
4.5%
good 10277
 
2.5%
for 9784
 
2.4%
works 9534
 
2.4%
the 9221
 
2.3%
it 7932
 
2.0%
to 7697
 
1.9%
and 6653
 
1.6%
a 6315
 
1.6%
not 6242
 
1.5%
Other values (12347) 312276
77.3%
2025-01-16T13:36:25.291414image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
305823
13.9%
e 215592
 
9.8%
t 165993
 
7.5%
o 162733
 
7.4%
r 129550
 
5.9%
a 128734
 
5.8%
s 106073
 
4.8%
i 97227
 
4.4%
n 90032
 
4.1%
d 76606
 
3.5%
Other values (54) 725323
32.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2203686
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
305823
13.9%
e 215592
 
9.8%
t 165993
 
7.5%
o 162733
 
7.4%
r 129550
 
5.9%
a 128734
 
5.8%
s 106073
 
4.8%
i 97227
 
4.4%
n 90032
 
4.1%
d 76606
 
3.5%
Other values (54) 725323
32.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2203686
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
305823
13.9%
e 215592
 
9.8%
t 165993
 
7.5%
o 162733
 
7.4%
r 129550
 
5.9%
a 128734
 
5.8%
s 106073
 
4.8%
i 97227
 
4.4%
n 90032
 
4.1%
d 76606
 
3.5%
Other values (54) 725323
32.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2203686
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
305823
13.9%
e 215592
 
9.8%
t 165993
 
7.5%
o 162733
 
7.4%
r 129550
 
5.9%
a 128734
 
5.8%
s 106073
 
4.8%
i 97227
 
4.4%
n 90032
 
4.1%
d 76606
 
3.5%
Other values (54) 725323
32.9%
Distinct93821
Distinct (%)91.6%
Missing47
Missing (%)< 0.1%
Memory size800.6 KiB
2025-01-16T13:36:26.121813image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length6251
Median length2198
Mean length166.38727
Min length1

Characters and Unicode

Total characters17041051
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique92022 ?
Unique (%)89.8%

Sample

1st rowThis radio was perfect for my father Hes older in his 80s and he wanted a simple transistor radio for the bathroom that runs on batteries He didnt want anything too fancy or expensive This fits the bill
2nd rowI couldnt get any stations in , worthless to me YouTube videos why I bought it buyer beware
3rd rowThis affordable radio is perfect for my needs Yet, I miss the quality from the higher end Sony portable Sorry, the sound is a bit tinny Yet, I am a fan of the controls, display and design Good value
4th rowPROS and CONS and why I chose this one The storyMy mother still lives in independent living with her older sister shes turning 90, her sister 92She couldnt get visitors for her birthday because all independent and assisted living places are on lockdown with whats going onSO I had to find something EASY for her to set up on her own, that was small for her bedside table, and would be a goodsounding radio EUREKAPROSPACKAGING I was concerned re how it would arrive, but my mother said it was in heavy foam packaging Came in perfect conditionSIZE PERFECT SMALL RADIO for her night stand MUCH smaller than the 22 inch dimensions it shows in top description Its small enough for a SMALL nightstandSET UP MY mother had no problem setting it up or figuring out knobs This is as easy as radios from the 60s Basically a couple knobs and a switch to go back and forth between AM and FMSOUND I was on the phone with my mother as she dialed through various stations and GREAT sound She was delighted and said, HEAR THISPRICE CANT BEAT THE PRICE
5th rowWaste of money,,,,, cant get stations on this radio as clear as others
ValueCountFrequency (%)
the 146656
 
4.5%
i 100056
 
3.1%
to 97459
 
3.0%
and 92646
 
2.9%
it 85329
 
2.6%
a 75673
 
2.3%
for 50761
 
1.6%
this 50436
 
1.6%
is 48960
 
1.5%
my 48162
 
1.5%
Other values (51021) 2454545
75.5%
2025-01-16T13:36:27.286289image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3202769
18.8%
e 1587360
 
9.3%
t 1315180
 
7.7%
o 1091172
 
6.4%
a 992176
 
5.8%
s 856929
 
5.0%
i 852764
 
5.0%
n 822764
 
4.8%
r 760977
 
4.5%
h 635003
 
3.7%
Other values (56) 4923957
28.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17041051
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3202769
18.8%
e 1587360
 
9.3%
t 1315180
 
7.7%
o 1091172
 
6.4%
a 992176
 
5.8%
s 856929
 
5.0%
i 852764
 
5.0%
n 822764
 
4.8%
r 760977
 
4.5%
h 635003
 
3.7%
Other values (56) 4923957
28.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17041051
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3202769
18.8%
e 1587360
 
9.3%
t 1315180
 
7.7%
o 1091172
 
6.4%
a 992176
 
5.8%
s 856929
 
5.0%
i 852764
 
5.0%
n 822764
 
4.8%
r 760977
 
4.5%
h 635003
 
3.7%
Other values (56) 4923957
28.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17041051
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3202769
18.8%
e 1587360
 
9.3%
t 1315180
 
7.7%
o 1091172
 
6.4%
a 992176
 
5.8%
s 856929
 
5.0%
i 852764
 
5.0%
n 822764
 
4.8%
r 760977
 
4.5%
h 635003
 
3.7%
Other values (56) 4923957
28.9%
Distinct1310
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size800.6 KiB
Minimum2020-01-01 00:00:00
Maximum2023-08-25 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-16T13:36:27.485473image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:27.702392image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Helpfulness
Real number (ℝ)

Skewed  Zeros 

Distinct65
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21727419
Minimum0
Maximum589
Zeros92646
Zeros (%)90.4%
Negative0
Negative (%)0.0%
Memory size800.6 KiB
2025-01-16T13:36:27.912059image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum589
Range589
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.0295911
Coefficient of variation (CV)13.943631
Kurtosis15975.072
Mean0.21727419
Median Absolute Deviation (MAD)0
Skewness102.40165
Sum22263
Variance9.1784219
MonotonicityNot monotonic
2025-01-16T13:36:28.105946image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 92646
90.4%
1 7134
 
7.0%
2 1304
 
1.3%
3 469
 
0.5%
4 260
 
0.3%
5 157
 
0.2%
6 91
 
0.1%
7 80
 
0.1%
8 50
 
< 0.1%
9 36
 
< 0.1%
Other values (55) 238
 
0.2%
ValueCountFrequency (%)
0 92646
90.4%
1 7134
 
7.0%
2 1304
 
1.3%
3 469
 
0.5%
4 260
 
0.3%
5 157
 
0.2%
6 91
 
0.1%
7 80
 
0.1%
8 50
 
< 0.1%
9 36
 
< 0.1%
ValueCountFrequency (%)
589 1
< 0.1%
283 1
< 0.1%
243 1
< 0.1%
235 1
< 0.1%
189 1
< 0.1%
174 1
< 0.1%
136 1
< 0.1%
132 1
< 0.1%
118 1
< 0.1%
117 1
< 0.1%

has_images
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size800.6 KiB
0
98559 
1
 
3906

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters102465
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 98559
96.2%
1 3906
 
3.8%

Length

2025-01-16T13:36:28.314982image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-16T13:36:28.482393image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 98559
96.2%
1 3906
 
3.8%

Most occurring characters

ValueCountFrequency (%)
0 98559
96.2%
1 3906
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 102465
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 98559
96.2%
1 3906
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 102465
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 98559
96.2%
1 3906
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 102465
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 98559
96.2%
1 3906
 
3.8%

price
Real number (ℝ)

Distinct53
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.813404
Minimum5.99
Maximum175.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size800.6 KiB
2025-01-16T13:36:28.634186image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum5.99
5-th percentile6.99
Q111.99
median19.99
Q335.99
95-th percentile87.14
Maximum175.99
Range170
Interquartile range (IQR)24

Descriptive statistics

Standard deviation27.778812
Coefficient of variation (CV)0.96409337
Kurtosis9.3664763
Mean28.813404
Median Absolute Deviation (MAD)10
Skewness2.7096487
Sum2952365.5
Variance771.66241
MonotonicityNot monotonic
2025-01-16T13:36:28.821345image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.99 8363
 
8.2%
13.99 5791
 
5.7%
29.99 4453
 
4.3%
11.99 4023
 
3.9%
9.99 4005
 
3.9%
15.99 3701
 
3.6%
23.99 2897
 
2.8%
37.99 2833
 
2.8%
39.99 2705
 
2.6%
8.97 2603
 
2.5%
Other values (43) 61091
59.6%
ValueCountFrequency (%)
5.99 1429
1.4%
6.36 1099
1.1%
6.44 1290
1.3%
6.99 2257
2.2%
7.82 1140
1.1%
7.95 1619
1.6%
7.99 1304
1.3%
8.54 1164
1.1%
8.97 2603
2.5%
8.99 2202
2.1%
ValueCountFrequency (%)
175.99 1251
1.2%
118 1444
1.4%
101 1421
1.4%
87.14 1132
1.1%
79.99 1414
1.4%
74.95 1084
1.1%
59.9 1837
1.8%
57 1262
1.2%
55.96 1468
1.4%
51 1367
1.3%

Day_elapsed
Real number (ℝ)

Distinct1333
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean736.50369
Minimum0
Maximum1332
Zeros81
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size800.6 KiB
2025-01-16T13:36:29.029080image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile122
Q1476
median784
Q31024
95-th percentile1191
Maximum1332
Range1332
Interquartile range (IQR)548

Descriptive statistics

Standard deviation339.48042
Coefficient of variation (CV)0.46093512
Kurtosis-0.915139
Mean736.50369
Median Absolute Deviation (MAD)264
Skewness-0.37981747
Sum75465851
Variance115246.96
MonotonicityNot monotonic
2025-01-16T13:36:29.232017image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1167 179
 
0.2%
1164 175
 
0.2%
1157 174
 
0.2%
1159 174
 
0.2%
1177 168
 
0.2%
1183 165
 
0.2%
1166 165
 
0.2%
1171 162
 
0.2%
1175 160
 
0.2%
1161 159
 
0.2%
Other values (1323) 100784
98.4%
ValueCountFrequency (%)
0 81
0.1%
1 12
 
< 0.1%
2 12
 
< 0.1%
3 11
 
< 0.1%
4 10
 
< 0.1%
5 14
 
< 0.1%
6 24
 
< 0.1%
7 20
 
< 0.1%
8 23
 
< 0.1%
9 24
 
< 0.1%
ValueCountFrequency (%)
1332 3
 
< 0.1%
1331 2
 
< 0.1%
1330 10
< 0.1%
1329 7
< 0.1%
1328 4
 
< 0.1%
1327 8
< 0.1%
1326 12
< 0.1%
1325 13
< 0.1%
1324 12
< 0.1%
1323 10
< 0.1%

depth
Real number (ℝ)

High correlation 

Distinct86899
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-53.368925
Minimum-100
Maximum-7.3141478
Zeros0
Zeros (%)0.0%
Negative102465
Negative (%)100.0%
Memory size800.6 KiB
2025-01-16T13:36:29.690404image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile-81.857718
Q1-64.754067
median-48.226358
Q3-45.116872
95-th percentile-27.156242
Maximum-7.3141478
Range92.685852
Interquartile range (IQR)19.637195

Descriptive statistics

Standard deviation19.051078
Coefficient of variation (CV)-0.3569695
Kurtosis-0.52340901
Mean-53.368925
Median Absolute Deviation (MAD)15.865586
Skewness0.021449515
Sum-5468446.9
Variance362.94358
MonotonicityNot monotonic
2025-01-16T13:36:29.902699image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-100 1180
 
1.2%
-62.15480079 669
 
0.7%
-80.84890017 657
 
0.6%
-80.84250123 562
 
0.5%
-64.43692464 447
 
0.4%
-81.44470947 414
 
0.4%
-63.13134616 366
 
0.4%
-62.79221066 240
 
0.2%
-64.09818971 213
 
0.2%
-63.51209851 192
 
0.2%
Other values (86889) 97525
95.2%
ValueCountFrequency (%)
-100 1180
1.2%
-83.96139282 5
 
< 0.1%
-83.83256032 6
 
< 0.1%
-83.8174782 12
 
< 0.1%
-83.8085442 3
 
< 0.1%
-83.7836055 2
 
< 0.1%
-83.7780258 16
 
< 0.1%
-83.7650551 2
 
< 0.1%
-83.75760226 7
 
< 0.1%
-83.75475802 18
 
< 0.1%
ValueCountFrequency (%)
-7.314147829 1
< 0.1%
-7.546417695 1
< 0.1%
-7.626836017 1
< 0.1%
-7.855493736 1
< 0.1%
-7.876928883 1
< 0.1%
-7.902762854 1
< 0.1%
-7.982638707 1
< 0.1%
-7.985318326 1
< 0.1%
-7.997865551 1
< 0.1%
-8.004889037 1
< 0.1%

breadth
Real number (ℝ)

High correlation 

Distinct74762
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0580236
Minimum0.013189577
Maximum3.3429617
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size800.6 KiB
2025-01-16T13:36:30.103859image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.013189577
5-th percentile0.35356909
Q10.57288856
median0.80398825
Q31.3206125
95-th percentile2.8249245
Maximum3.3429617
Range3.3297722
Interquartile range (IQR)0.74772399

Descriptive statistics

Standard deviation0.74815671
Coefficient of variation (CV)0.70712665
Kurtosis1.295468
Mean1.0580236
Median Absolute Deviation (MAD)0.27910265
Skewness1.4700128
Sum108410.39
Variance0.55973846
MonotonicityNot monotonic
2025-01-16T13:36:30.302552image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8039882472 11828
 
11.5%
0.4217663033 1180
 
1.2%
0.8039882472 1167
 
1.1%
3.229167424 761
 
0.7%
2.326794638 674
 
0.7%
3.342961744 663
 
0.6%
3.155294562 538
 
0.5%
3.153489358 453
 
0.4%
2.731573984 366
 
0.4%
2.696855083 241
 
0.2%
Other values (74752) 84594
82.6%
ValueCountFrequency (%)
0.01318957667 1
< 0.1%
0.02141185931 1
< 0.1%
0.02321670867 1
< 0.1%
0.02377770131 1
< 0.1%
0.02410144519 1
< 0.1%
0.02616735167 1
< 0.1%
0.02738159501 1
< 0.1%
0.03015226037 1
< 0.1%
0.03100424373 1
< 0.1%
0.0313369661 1
< 0.1%
ValueCountFrequency (%)
3.342961744 663
0.6%
3.342834319 1
 
< 0.1%
3.34206396 1
 
< 0.1%
3.34121746 1
 
< 0.1%
3.340538254 1
 
< 0.1%
3.340027091 1
 
< 0.1%
3.339597232 2
 
< 0.1%
3.338871513 1
 
< 0.1%
3.332836022 3
 
< 0.1%
3.331944298 1
 
< 0.1%

Topic_1
Real number (ℝ)

Distinct86880
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11029773
Minimum4.147535 × 10-20
Maximum0.99720939
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size800.6 KiB
2025-01-16T13:36:30.514951image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum4.147535 × 10-20
5-th percentile1.2381049 × 10-19
Q12.8978099 × 10-19
median1.2586995 × 10-18
Q30.13796868
95-th percentile0.58000003
Maximum0.99720939
Range0.99720939
Interquartile range (IQR)0.13796868

Descriptive statistics

Standard deviation0.2049502
Coefficient of variation (CV)1.8581542
Kurtosis5.2897402
Mean0.11029773
Median Absolute Deviation (MAD)1.188084 × 10-18
Skewness2.3328309
Sum11301.657
Variance0.042004584
MonotonicityNot monotonic
2025-01-16T13:36:30.723696image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 1180
 
1.2%
0.3787096587 673
 
0.7%
7.061552133 × 10-20657
 
0.6%
6.958269283 × 10-20562
 
0.5%
0.9964347246 453
 
0.4%
2.784257975 × 10-19414
 
0.4%
0.9029504554 366
 
0.4%
0.1192807054 241
 
0.2%
4.493223647 × 10-19213
 
0.2%
6.87955492 × 10-20192
 
0.2%
Other values (86870) 97514
95.2%
ValueCountFrequency (%)
4.147535011 × 10-202
 
< 0.1%
4.268259352 × 10-203
 
< 0.1%
4.489099647 × 10-201
 
< 0.1%
4.664085127 × 10-201
 
< 0.1%
4.920291367 × 10-201
 
< 0.1%
4.954693228 × 10-201
 
< 0.1%
4.955025616 × 10-201
 
< 0.1%
4.965180013 × 10-2083
0.1%
4.978253443 × 10-202
 
< 0.1%
5.059042651 × 10-2013
 
< 0.1%
ValueCountFrequency (%)
0.9972093882 13
< 0.1%
0.9969672982 4
 
< 0.1%
0.9969358365 1
 
< 0.1%
0.9969175778 8
< 0.1%
0.9967185579 1
 
< 0.1%
0.996697176 1
 
< 0.1%
0.9966875567 2
 
< 0.1%
0.9966837774 1
 
< 0.1%
0.9966745614 2
 
< 0.1%
0.9966675132 3
 
< 0.1%

Topic_2
Real number (ℝ)

Distinct86831
Distinct (%)84.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10694872
Minimum4.4834084 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size800.6 KiB
2025-01-16T13:36:30.937449image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum4.4834084 × 10-20
5-th percentile1.1591285 × 10-19
Q12.7539732 × 10-19
median7.3266144 × 10-19
Q30.097049545
95-th percentile0.64305081
Maximum1
Range1
Interquartile range (IQR)0.097049545

Descriptive statistics

Standard deviation0.21711927
Coefficient of variation (CV)2.030125
Kurtosis5.613712
Mean0.10694872
Median Absolute Deviation (MAD)6.1220301 × 10-19
Skewness2.4702795
Sum10958.501
Variance0.047140777
MonotonicityNot monotonic
2025-01-16T13:36:31.148221image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 1180
 
1.2%
1 761
 
0.7%
5.796967792 × 10-20669
 
0.7%
6.958269283 × 10-20562
 
0.5%
9.856739732 × 10-20447
 
0.4%
2.784257975 × 10-19414
 
0.4%
0.09704954462 366
 
0.4%
0.8807192946 241
 
0.2%
4.493223647 × 10-19213
 
0.2%
6.87955492 × 10-20192
 
0.2%
Other values (86821) 97420
95.1%
ValueCountFrequency (%)
4.483408352 × 10-204
 
< 0.1%
4.541514264 × 10-2026
< 0.1%
4.569832018 × 10-201
 
< 0.1%
4.657171629 × 10-2053
0.1%
4.87253231 × 10-201
 
< 0.1%
4.955025616 × 10-201
 
< 0.1%
4.958027805 × 10-201
 
< 0.1%
4.966478569 × 10-201
 
< 0.1%
5.032363279 × 10-201
 
< 0.1%
5.059042651 × 10-2013
 
< 0.1%
ValueCountFrequency (%)
1 761
0.7%
0.999949551 1
 
< 0.1%
0.9997966969 1
 
< 0.1%
0.9997416009 1
 
< 0.1%
0.9997369537 1
 
< 0.1%
0.999734747 1
 
< 0.1%
0.9997024913 2
 
< 0.1%
0.9995200362 1
 
< 0.1%
0.9994864076 2
 
< 0.1%
0.9994287136 2
 
< 0.1%

Topic_3
Real number (ℝ)

Distinct86840
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.098841506
Minimum4.9782534 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size800.6 KiB
2025-01-16T13:36:31.347847image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum4.9782534 × 10-20
5-th percentile1.2219941 × 10-19
Q12.784258 × 10-19
median1.2641121 × 10-18
Q30.04109451
95-th percentile0.62129034
Maximum1
Range1
Interquartile range (IQR)0.04109451

Descriptive statistics

Standard deviation0.21411136
Coefficient of variation (CV)2.166209
Kurtosis5.5070627
Mean0.098841506
Median Absolute Deviation (MAD)1.1824208 × 10-18
Skewness2.4751266
Sum10127.795
Variance0.045843673
MonotonicityNot monotonic
2025-01-16T13:36:31.575050image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 1180
 
1.2%
0.6212903413 674
 
0.7%
1 663
 
0.6%
7.061552133 × 10-20657
 
0.6%
0.003565275424 452
 
0.4%
2.784257975 × 10-19414
 
0.4%
1.088934587 × 10-19366
 
0.4%
8.068804389 × 10-20240
 
0.2%
0.2017337698 213
 
0.2%
6.87955492 × 10-20192
 
0.2%
Other values (86830) 97414
95.1%
ValueCountFrequency (%)
4.978253443 × 10-202
 
< 0.1%
5.056807158 × 10-201
 
< 0.1%
5.061066349 × 10-201
 
< 0.1%
5.175948012 × 10-201
 
< 0.1%
5.196888555 × 10-205
< 0.1%
5.309432565 × 10-202
 
< 0.1%
5.316742399 × 10-204
< 0.1%
5.327206717 × 10-203
< 0.1%
5.407800269 × 10-201
 
< 0.1%
5.423471178 × 10-201
 
< 0.1%
ValueCountFrequency (%)
1 663
0.6%
0.9999940273 1
 
< 0.1%
0.9999429137 1
 
< 0.1%
0.9998804142 1
 
< 0.1%
0.9998276128 1
 
< 0.1%
0.9997865755 1
 
< 0.1%
0.9997513249 2
 
< 0.1%
0.9996904585 1
 
< 0.1%
0.9991608211 1
 
< 0.1%
0.999137088 3
 
< 0.1%

Topic_4
Real number (ℝ)

High correlation 

Distinct75039
Distinct (%)73.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57139936
Minimum4.147535 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size800.6 KiB
2025-01-16T13:36:31.795418image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum4.147535 × 10-20
5-th percentile1.1623154 × 10-19
Q10.24806883
median0.60056884
Q30.93351149
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.68544266

Descriptive statistics

Standard deviation0.35116619
Coefficient of variation (CV)0.61457226
Kurtosis-1.3974521
Mean0.57139936
Median Absolute Deviation (MAD)0.34082277
Skewness-0.21853555
Sum58548.435
Variance0.1233177
MonotonicityNot monotonic
2025-01-16T13:36:31.979191image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 13707
 
13.4%
0.2 1180
 
1.2%
5.796967792 × 10-20669
 
0.7%
7.061552133 × 10-20657
 
0.6%
6.958269283 × 10-20562
 
0.5%
9.856739732 × 10-20447
 
0.4%
1.088934587 × 10-19366
 
0.4%
8.068804389 × 10-20240
 
0.2%
0.7982662302 213
 
0.2%
0.01477359288 192
 
0.2%
Other values (75029) 84232
82.2%
ValueCountFrequency (%)
4.147535011 × 10-202
 
< 0.1%
4.268259352 × 10-203
 
< 0.1%
4.483408352 × 10-204
 
< 0.1%
4.489099647 × 10-201
 
< 0.1%
4.541514264 × 10-2026
< 0.1%
4.569832018 × 10-201
 
< 0.1%
4.588572266 × 10-201
 
< 0.1%
4.657171629 × 10-2053
0.1%
4.678108418 × 10-201
 
< 0.1%
4.783962815 × 10-201
 
< 0.1%
ValueCountFrequency (%)
1 13707
13.4%
1 97
 
0.1%
1 5
 
< 0.1%
0.9999969657 1
 
< 0.1%
0.9999918825 1
 
< 0.1%
0.9999902135 1
 
< 0.1%
0.9999891108 1
 
< 0.1%
0.9999888429 1
 
< 0.1%
0.9999830744 1
 
< 0.1%
0.9999806559 1
 
< 0.1%

Topic_5
Real number (ℝ)

Distinct86700
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11251268
Minimum4.6781084 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size800.6 KiB
2025-01-16T13:36:32.203288image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum4.6781084 × 10-20
5-th percentile1.0889346 × 10-19
Q12.6245022 × 10-19
median6.8610217 × 10-19
Q30.11082765
95-th percentile0.66144492
Maximum1
Range1
Interquartile range (IQR)0.11082765

Descriptive statistics

Standard deviation0.22278292
Coefficient of variation (CV)1.9800694
Kurtosis4.7509571
Mean0.11251268
Median Absolute Deviation (MAD)5.7277626 × 10-19
Skewness2.3199829
Sum11528.612
Variance0.049632228
MonotonicityNot monotonic
2025-01-16T13:36:32.429652image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 1180
 
1.2%
5.796967792 × 10-20669
 
0.7%
7.061552133 × 10-20657
 
0.6%
6.958269283 × 10-20562
 
0.5%
1 538
 
0.5%
9.856739732 × 10-20447
 
0.4%
2.784257975 × 10-19414
 
0.4%
1.088934587 × 10-19366
 
0.4%
8.068804389 × 10-20240
 
0.2%
4.493223647 × 10-19213
 
0.2%
Other values (86690) 97179
94.8%
ValueCountFrequency (%)
4.678108418 × 10-201
 
< 0.1%
4.963602213 × 10-207
 
< 0.1%
4.965180013 × 10-2083
0.1%
5.122504142 × 10-209
 
< 0.1%
5.23708597 × 10-203
 
< 0.1%
5.288316653 × 10-207
 
< 0.1%
5.342610105 × 10-201
 
< 0.1%
5.456151839 × 10-201
 
< 0.1%
5.462142388 × 10-201
 
< 0.1%
5.475498772 × 10-201
 
< 0.1%
ValueCountFrequency (%)
1 538
0.5%
0.9999879284 1
 
< 0.1%
0.9999750175 1
 
< 0.1%
0.9999308645 1
 
< 0.1%
0.9999145486 1
 
< 0.1%
0.9998761313 1
 
< 0.1%
0.9998655154 1
 
< 0.1%
0.9998610455 1
 
< 0.1%
0.9998356783 1
 
< 0.1%
0.99981854 1
 
< 0.1%

sentiment_score_continuous
Real number (ℝ)

High correlation  Missing 

Distinct3978
Distinct (%)4.2%
Missing8328
Missing (%)8.1%
Infinite0
Infinite (%)0.0%
Mean3.7878842
Minimum1.011
Maximum4.991
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size800.6 KiB
2025-01-16T13:36:32.878737image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1.011
5-th percentile1.348
Q13.033
median4.322
Q34.708
95-th percentile4.898
Maximum4.991
Range3.98
Interquartile range (IQR)1.675

Descriptive statistics

Standard deviation1.1910497
Coefficient of variation (CV)0.31443667
Kurtosis-0.41559682
Mean3.7878842
Median Absolute Deviation (MAD)0.476
Skewness-1.0120795
Sum356580.05
Variance1.4185994
MonotonicityNot monotonic
2025-01-16T13:36:33.091341image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.618 430
 
0.4%
4.103 397
 
0.4%
4.682 336
 
0.3%
4.734 279
 
0.3%
4.797 267
 
0.3%
4.794 218
 
0.2%
4.832 203
 
0.2%
4.783 199
 
0.2%
4.831 192
 
0.2%
4.707 191
 
0.2%
Other values (3968) 91425
89.2%
(Missing) 8328
 
8.1%
ValueCountFrequency (%)
1.011 1
 
< 0.1%
1.013 3
 
< 0.1%
1.015 4
< 0.1%
1.016 2
 
< 0.1%
1.017 8
< 0.1%
1.019 6
< 0.1%
1.02 1
 
< 0.1%
1.021 5
< 0.1%
1.022 5
< 0.1%
1.023 9
< 0.1%
ValueCountFrequency (%)
4.991 1
 
< 0.1%
4.99 2
< 0.1%
4.989 1
 
< 0.1%
4.988 2
< 0.1%
4.987 2
< 0.1%
4.986 4
< 0.1%
4.985 3
< 0.1%
4.984 1
 
< 0.1%
4.983 2
< 0.1%
4.982 3
< 0.1%

sentiment_score_discrete
Categorical

High correlation  Missing 

Distinct5
Distinct (%)< 0.1%
Missing8328
Missing (%)8.1%
Memory size800.6 KiB
5.0
48784 
4.0
18548 
1.0
11572 
2.0
7820 
3.0
7413 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters282411
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row1.0
3rd row4.0
4th row5.0
5th row1.0

Common Values

ValueCountFrequency (%)
5.0 48784
47.6%
4.0 18548
 
18.1%
1.0 11572
 
11.3%
2.0 7820
 
7.6%
3.0 7413
 
7.2%
(Missing) 8328
 
8.1%

Length

2025-01-16T13:36:33.275193image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-16T13:36:33.465748image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
5.0 48784
51.8%
4.0 18548
 
19.7%
1.0 11572
 
12.3%
2.0 7820
 
8.3%
3.0 7413
 
7.9%

Most occurring characters

ValueCountFrequency (%)
. 94137
33.3%
0 94137
33.3%
5 48784
17.3%
4 18548
 
6.6%
1 11572
 
4.1%
2 7820
 
2.8%
3 7413
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 282411
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 94137
33.3%
0 94137
33.3%
5 48784
17.3%
4 18548
 
6.6%
1 11572
 
4.1%
2 7820
 
2.8%
3 7413
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 282411
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 94137
33.3%
0 94137
33.3%
5 48784
17.3%
4 18548
 
6.6%
1 11572
 
4.1%
2 7820
 
2.8%
3 7413
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 282411
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 94137
33.3%
0 94137
33.3%
5 48784
17.3%
4 18548
 
6.6%
1 11572
 
4.1%
2 7820
 
2.8%
3 7413
 
2.6%

sst5_sentiment_score
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing90
Missing (%)0.1%
Memory size800.6 KiB
4.0
58199 
2.0
16238 
3.0
13934 
5.0
12154 
1.0
 
1850

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters307125
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row2.0
3rd row4.0
4th row4.0
5th row2.0

Common Values

ValueCountFrequency (%)
4.0 58199
56.8%
2.0 16238
 
15.8%
3.0 13934
 
13.6%
5.0 12154
 
11.9%
1.0 1850
 
1.8%
(Missing) 90
 
0.1%

Length

2025-01-16T13:36:33.628436image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-16T13:36:33.829119image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
4.0 58199
56.8%
2.0 16238
 
15.9%
3.0 13934
 
13.6%
5.0 12154
 
11.9%
1.0 1850
 
1.8%

Most occurring characters

ValueCountFrequency (%)
. 102375
33.3%
0 102375
33.3%
4 58199
18.9%
2 16238
 
5.3%
3 13934
 
4.5%
5 12154
 
4.0%
1 1850
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 307125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 102375
33.3%
0 102375
33.3%
4 58199
18.9%
2 16238
 
5.3%
3 13934
 
4.5%
5 12154
 
4.0%
1 1850
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 307125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 102375
33.3%
0 102375
33.3%
4 58199
18.9%
2 16238
 
5.3%
3 13934
 
4.5%
5 12154
 
4.0%
1 1850
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 307125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 102375
33.3%
0 102375
33.3%
4 58199
18.9%
2 16238
 
5.3%
3 13934
 
4.5%
5 12154
 
4.0%
1 1850
 
0.6%

text_length
Real number (ℝ)

High correlation 

Distinct475
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.725272
Minimum0
Maximum1066
Zeros90
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size800.6 KiB
2025-01-16T13:36:34.012924image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q18
median19
Q340
95-th percentile103
Maximum1066
Range1066
Interquartile range (IQR)32

Descriptive statistics

Standard deviation41.353401
Coefficient of variation (CV)1.3034845
Kurtosis43.907116
Mean31.725272
Median Absolute Deviation (MAD)13
Skewness4.5845795
Sum3250730
Variance1710.1038
MonotonicityNot monotonic
2025-01-16T13:36:34.225616image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 5231
 
5.1%
5 3703
 
3.6%
4 3584
 
3.5%
6 3509
 
3.4%
3 3309
 
3.2%
7 3247
 
3.2%
8 2886
 
2.8%
9 2779
 
2.7%
10 2720
 
2.7%
11 2608
 
2.5%
Other values (465) 68889
67.2%
ValueCountFrequency (%)
0 90
 
0.1%
1 2526
2.5%
2 5231
5.1%
3 3309
3.2%
4 3584
3.5%
5 3703
3.6%
6 3509
3.4%
7 3247
3.2%
8 2886
2.8%
9 2779
2.7%
ValueCountFrequency (%)
1066 1
< 0.1%
1050 1
< 0.1%
976 1
< 0.1%
884 1
< 0.1%
813 1
< 0.1%
778 1
< 0.1%
777 1
< 0.1%
768 1
< 0.1%
763 1
< 0.1%
740 1
< 0.1%

Deviation of star ratings
Real number (ℝ)

High correlation 

Distinct45
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.97486849
Minimum0
Maximum3.9
Zeros125
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size800.6 KiB
2025-01-16T13:36:34.440467image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.4
median0.5
Q30.9
95-th percentile3.6
Maximum3.9
Range3.9
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation1.0390686
Coefficient of variation (CV)1.0658552
Kurtosis1.1987323
Mean0.97486849
Median Absolute Deviation (MAD)0.2
Skewness1.6349104
Sum99889.9
Variance1.0796636
MonotonicityNot monotonic
2025-01-16T13:36:34.649806image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0.4 22222
21.7%
0.3 18860
18.4%
0.5 10478
10.2%
0.7 8784
 
8.6%
0.6 6863
 
6.7%
0.2 4204
 
4.1%
3.6 2940
 
2.9%
0.8 1953
 
1.9%
3.3 1936
 
1.9%
3.7 1896
 
1.9%
Other values (35) 22329
21.8%
ValueCountFrequency (%)
0 125
 
0.1%
0.1 1217
 
1.2%
0.1 253
 
0.2%
0.2 4204
 
4.1%
0.3 18860
18.4%
0.4 22222
21.7%
0.5 10478
10.2%
0.6 6863
 
6.7%
0.7 8784
 
8.6%
0.8 1953
 
1.9%
ValueCountFrequency (%)
3.9 20
 
< 0.1%
3.8 413
 
0.4%
3.7 1896
1.9%
3.6 2940
2.9%
3.5 1390
1.4%
3.4 929
 
0.9%
3.3 1936
1.9%
3.2 402
 
0.4%
3.1 666
 
0.6%
3 185
 
0.2%

FOG Index
Real number (ℝ)

High correlation 

Distinct2646
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.455922
Minimum0
Maximum428.24
Zeros90
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size800.6 KiB
2025-01-16T13:36:34.875281image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.8
Q15.6
median11.6
Q319.42
95-th percentile43.52
Maximum428.24
Range428.24
Interquartile range (IQR)13.82

Descriptive statistics

Standard deviation16.725222
Coefficient of variation (CV)1.0821238
Kurtosis41.221048
Mean15.455922
Median Absolute Deviation (MAD)6.8
Skewness4.3221596
Sum1583691.1
Variance279.73304
MonotonicityNot monotonic
2025-01-16T13:36:35.073555image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8 4222
 
4.1%
2 2422
 
2.4%
1.6 2385
 
2.3%
1.2 2378
 
2.3%
2.4 2188
 
2.1%
0.4 2127
 
2.1%
10 1994
 
1.9%
8.04 1842
 
1.8%
2.8 1823
 
1.8%
11.6 1653
 
1.6%
Other values (2636) 79431
77.5%
ValueCountFrequency (%)
0 90
 
0.1%
0.4 2127
2.1%
0.8 4222
4.1%
1.2 2378
2.3%
1.6 2385
2.3%
2 2422
2.4%
2.4 2188
2.1%
2.8 1823
1.8%
3.2 1551
 
1.5%
3.6 1451
 
1.4%
ValueCountFrequency (%)
428.24 1
< 0.1%
422.8 1
< 0.1%
391.59 1
< 0.1%
354.78 1
< 0.1%
326.73 1
< 0.1%
313.27 1
< 0.1%
312.96 1
< 0.1%
308.71 1
< 0.1%
307.24 1
< 0.1%
297.3 1
< 0.1%

Flesch Reading Ease
Real number (ℝ)

High correlation 

Distinct1451
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.896143
Minimum-985.14
Maximum206.84
Zeros0
Zeros (%)0.0%
Negative7533
Negative (%)7.4%
Memory size800.6 KiB
2025-01-16T13:36:35.288049image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-985.14
5-th percentile-17.178
Q145.43
median68.44
Q387.05
95-th percentile119.19
Maximum206.84
Range1191.98
Interquartile range (IQR)41.62

Descriptive statistics

Standard deviation46.493616
Coefficient of variation (CV)0.76349032
Kurtosis28.863172
Mean60.896143
Median Absolute Deviation (MAD)20.3
Skewness-3.298223
Sum6239723.2
Variance2161.6563
MonotonicityNot monotonic
2025-01-16T13:36:35.515033image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120.21 2529
 
2.5%
77.91 1733
 
1.7%
121.22 1635
 
1.6%
119.19 1176
 
1.1%
75.88 1071
 
1.0%
100.24 1050
 
1.0%
92.8 1040
 
1.0%
83.32 912
 
0.9%
99.23 903
 
0.9%
93.81 884
 
0.9%
Other values (1441) 89532
87.4%
ValueCountFrequency (%)
-985.14 1
< 0.1%
-976.35 1
< 0.1%
-893.79 1
< 0.1%
-800.41 1
< 0.1%
-728.35 1
< 0.1%
-708.73 1
< 0.1%
-700.27 1
< 0.1%
-682.67 1
< 0.1%
-677.6 1
< 0.1%
-654.25 1
< 0.1%
ValueCountFrequency (%)
206.84 90
 
0.1%
121.22 1635
1.6%
120.21 2529
2.5%
119.19 1176
1.1%
118.18 879
 
0.9%
117.16 672
 
0.7%
116.15 585
 
0.6%
115.13 427
 
0.4%
114.12 315
 
0.3%
113.1 218
 
0.2%

Interactions

2025-01-16T13:36:14.955507image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:29.094649image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:31.706098image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:34.269610image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:36.794673image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:39.288595image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:42.058740image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:44.435998image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:47.130324image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:49.561892image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:52.132820image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:54.854841image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:58.077765image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:01.468629image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:04.827736image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:08.229699image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:11.578432image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:15.148836image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:29.257519image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:31.832757image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:34.411064image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:36.942718image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:39.639312image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:42.207231image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:44.574815image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:47.267388image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:49.707508image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:52.275131image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:55.040879image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:58.267898image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:01.692230image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:05.018543image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:08.420090image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:11.764073image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:15.321745image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:29.396936image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:31.955572image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:34.560976image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:37.079992image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:39.778434image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:42.326071image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:44.703972image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:47.409711image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:49.840648image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:52.399428image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:55.214022image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:58.444019image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:01.852882image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:05.204450image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:08.614715image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:11.951305image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:15.499369image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:29.547780image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:32.106193image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:34.716478image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:37.231531image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:39.919073image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:42.473749image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:44.855327image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:47.551207image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:49.974328image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:52.545610image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:55.413218image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:58.873387image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:02.040317image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:05.404376image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:08.792443image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:12.122977image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:15.683223image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:29.713328image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:32.254527image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:34.875026image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:37.379710image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:40.074638image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:42.614694image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:45.006315image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:47.693300image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:50.125218image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:52.703632image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:55.618783image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:59.049458image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:02.265803image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:05.587702image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:09.007578image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:12.329139image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:15.874388image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:29.858878image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:32.393201image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:35.013525image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:37.518877image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:40.227252image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:42.756137image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:45.147841image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:47.862576image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:50.272010image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:52.849417image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:55.825123image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:59.248308image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:02.471491image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:05.788053image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:09.192422image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:12.511485image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:16.050421image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:30.032825image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:32.523438image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:35.146543image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:37.689095image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:40.372293image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:42.888221image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:45.491930image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:47.987700image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:50.401552image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:52.983323image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:56.012491image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:59.421621image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:02.648774image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:05.939847image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:09.390739image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:12.670054image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:16.248908image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:30.198813image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:32.678555image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:35.322664image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:37.839415image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:40.521449image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:43.034115image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:45.636730image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:48.137916image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:50.548944image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:53.129669image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:56.220816image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:59.613526image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:02.843056image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:06.110587image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:09.595922image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:12.856461image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:16.424197image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:30.332529image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:32.814332image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:35.468899image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:37.985716image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:40.676382image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:43.168216image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:45.783283image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:48.262659image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:50.685125image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:53.267656image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:56.379314image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:59.800541image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:03.047319image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:06.523394image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:09.775268image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:13.039410image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:16.613579image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:30.490411image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:32.952417image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:35.611088image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:38.130239image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:40.830557image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:43.308950image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:45.921140image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:48.399588image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:50.821892image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:53.415094image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:56.542512image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:59.979941image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:03.263562image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:06.707283image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:09.963759image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:13.231382image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:16.806543image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:30.634755image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:33.089103image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:35.750567image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:38.275584image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:40.962084image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:43.432376image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:46.070399image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:48.536578image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:50.944223image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:53.585213image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:56.744007image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:00.167008image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:03.446424image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:06.899865image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:10.174643image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:13.423619image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:17.007120image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:30.773085image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:33.220290image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:35.888425image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:38.418358image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:41.113743image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:43.564377image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:46.216026image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:48.666778image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:51.291955image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:53.753883image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:56.911433image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:00.332880image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:03.641404image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:07.079512image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:10.389788image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:13.609269image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:17.177287image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:30.934511image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:33.366419image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:36.027331image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:38.553921image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:41.254150image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:43.718911image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:46.369600image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:48.795412image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:51.419839image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:53.929441image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:57.102405image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:00.500338image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:03.832971image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:07.260940image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:10.589987image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:13.779768image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:17.363267image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:31.107489image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:33.520097image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:36.212565image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:38.711721image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:41.428790image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:43.877176image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:46.529237image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:48.953541image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:51.570379image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:54.115231image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:57.307796image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:00.702508image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:04.026983image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:07.462942image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:10.807620image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:14.246781image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:17.535514image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:31.248322image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:33.659127image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:36.354608image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:38.857584image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:41.562684image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:44.010350image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:46.688119image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:49.109997image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:51.720160image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:54.307865image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:57.489724image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:00.893561image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:04.212634image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:07.635253image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:10.994326image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:14.430614image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:17.745519image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:31.421242image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:33.813929image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:36.510999image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:39.000364image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:41.759089image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:44.156659image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:46.852643image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:49.269110image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:51.875643image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:54.494334image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:57.709303image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:01.096801image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:04.427367image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:07.868578image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:11.200265image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:14.616246image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:17.928906image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:31.551045image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:34.131502image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:36.649548image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:39.143307image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:41.893292image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:44.288649image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:46.991574image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:49.408110image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:52.000140image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:54.662522image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:35:57.888466image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:01.283625image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:04.620454image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:08.054192image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:11.371798image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:36:14.775508image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2025-01-16T13:36:35.744418image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Average_ratingDay_elapsedDeviation of star ratingsFOG IndexFlesch Reading EaseHelpfulnessNum_of_RatingRatingTopic_1Topic_2Topic_3Topic_4Topic_5breadthdepthhas_imagespricesentiment_score_continuoussentiment_score_discretesst5_sentiment_scoretext_length
Average_rating1.0000.078-0.558-0.0680.053-0.0290.2170.1230.013-0.0060.034-0.061-0.0110.0330.0160.080-0.0110.1760.1120.104-0.092
Day_elapsed0.0781.000-0.063-0.0380.029-0.087-0.1480.0380.0430.006-0.011-0.035-0.0340.0220.0230.035-0.0140.0840.0400.045-0.044
Deviation of star ratings-0.558-0.0631.0000.149-0.1270.149-0.1390.809-0.0550.017-0.0740.250-0.013-0.134-0.1450.0490.011-0.5460.4560.4290.194
FOG Index-0.068-0.0380.1491.000-0.8870.231-0.0940.061-0.0250.059-0.0740.1920.115-0.3790.2430.0950.112-0.1360.0780.0570.856
Flesch Reading Ease0.0530.029-0.127-0.8871.000-0.2150.0810.068-0.014-0.1170.061-0.176-0.1200.385-0.2120.097-0.1080.1120.0760.067-0.689
Helpfulness-0.029-0.0870.1490.231-0.2151.000-0.0220.003-0.0150.022-0.0440.1050.000-0.1340.0180.0300.053-0.1720.0050.0030.247
Num_of_Rating0.217-0.148-0.139-0.0940.081-0.0221.0000.052-0.020-0.0130.102-0.036-0.0330.045-0.0170.031-0.0610.0030.0490.049-0.106
Rating0.1230.0380.8090.0610.0680.0030.0521.0000.0920.0550.0770.1660.0670.1380.1280.0210.0750.5050.4930.4490.058
Topic_10.0130.043-0.055-0.025-0.014-0.015-0.0200.0921.0000.143-0.086-0.148-0.122-0.1890.3820.0410.0550.1790.1350.148-0.050
Topic_2-0.0060.0060.0170.059-0.1170.022-0.0130.0550.1431.000-0.283-0.066-0.160-0.1900.2620.0340.039-0.1090.1220.076-0.019
Topic_30.034-0.011-0.074-0.0740.061-0.0440.1020.077-0.086-0.2831.000-0.074-0.054-0.0440.1120.044-0.1500.0850.1020.096-0.092
Topic_4-0.061-0.0350.2500.192-0.1760.105-0.0360.166-0.148-0.066-0.0741.000-0.024-0.563-0.5270.0520.014-0.3190.1890.1870.242
Topic_5-0.011-0.034-0.0130.115-0.1200.000-0.0330.067-0.122-0.160-0.054-0.0241.000-0.1740.1710.0520.1270.0310.0830.0810.118
breadth0.0330.022-0.134-0.3790.385-0.1340.0450.138-0.189-0.190-0.044-0.563-0.1741.000-0.1120.058-0.0430.1690.1640.163-0.366
depth0.0160.023-0.1450.243-0.2120.018-0.0170.1280.3820.2620.112-0.5270.171-0.1121.0000.0560.0300.2730.1520.1530.231
has_images0.0800.0350.0490.0950.0970.0300.0310.0210.0410.0340.0440.0520.0520.0580.0561.0000.0770.0250.0230.0220.096
price-0.011-0.0140.0110.112-0.1080.053-0.0610.0750.0550.039-0.1500.0140.127-0.0430.0300.0771.000-0.0040.0680.0870.137
sentiment_score_continuous0.1760.084-0.546-0.1360.112-0.1720.0030.5050.179-0.1090.085-0.3190.0310.1690.2730.025-0.0041.0000.8150.557-0.197
sentiment_score_discrete0.1120.0400.4560.0780.0760.0050.0490.4930.1350.1220.1020.1890.0830.1640.1520.0230.0680.8151.0000.5010.075
sst5_sentiment_score0.1040.0450.4290.0570.0670.0030.0490.4490.1480.0760.0960.1870.0810.1630.1530.0220.0870.5570.5011.0000.055
text_length-0.092-0.0440.1940.856-0.6890.247-0.1060.058-0.050-0.019-0.0920.2420.118-0.3660.2310.0960.137-0.1970.0750.0551.000

Missing values

2025-01-16T13:36:18.252524image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-16T13:36:18.967520image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-01-16T13:36:19.700165image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

product_nameAverage_ratingNum_of_RatingRatingreview_titleReview_TextPosted_DateHelpfulnesshas_imagespriceDay_elapseddepthbreadthTopic_1Topic_2Topic_3Topic_4Topic_5sentiment_score_continuoussentiment_score_discretesst5_sentiment_scoretext_lengthDeviation of star ratingsFOG IndexFlesch Reading Ease
0Panasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.84.6276185.0Its a good productThis radio was perfect for my father Hes older in his 80s and he wanted a simple transistor radio for the bathroom that runs on batteries He didnt want anything too fancy or expensive This fits the bill2022-04-210034.95348-64.6551710.6692664.020516e-194.020516e-193.713893e-020.9628614.020516e-194.7535.04.0380.418.3649.83
1Panasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.84.6276181.0nothing came inI couldnt get any stations in , worthless to me YouTube videos why I bought it buyer beware2022-08-290034.95218-82.0118580.8039881.027679e-181.027679e-181.027679e-181.0000001.027679e-181.1321.02.0183.66.8079.60
2Panasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.84.6276183.0Great for basement or garage useThis affordable radio is perfect for my needs Yet, I miss the quality from the higher end Sony portable Sorry, the sound is a bit tinny Yet, I am a fan of the controls, display and design Good value2021-07-161034.95627-29.5752721.0791582.837599e-025.696896e-018.302376e-030.3936322.111245e-194.0024.04.0391.618.6848.81
3Panasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.84.6276185.0PROSCONS Good things come in SMALL packagesPROS and CONS and why I chose this one The storyMy mother still lives in independent living with her older sister shes turning 90, her sister 92She couldnt get visitors for her birthday because all independent and assisted living places are on lockdown with whats going onSO I had to find something EASY for her to set up on her own, that was small for her bedside table, and would be a goodsounding radio EUREKAPROSPACKAGING I was concerned re how it would arrive, but my mother said it was in heavy foam packaging Came in perfect conditionSIZE PERFECT SMALL RADIO for her night stand MUCH smaller than the 22 inch dimensions it shows in top description Its small enough for a SMALL nightstandSET UP MY mother had no problem setting it up or figuring out knobs This is as easy as radios from the 60s Basically a couple knobs and a switch to go back and forth between AM and FMSOUND I was on the phone with my mother as she dialed through various stations and GREAT sound She was delighted and said, HEAR THISPRICE CANT BEAT THE PRICE2020-04-080034.951091-10.5684440.4559521.146106e-011.800103e-019.582888e-030.3364063.593906e-014.3805.04.01890.478.56-103.45
4Panasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.84.6276181.0Doesnt pick up wellWaste of money,,,,, cant get stations on this radio as clear as others2020-12-250034.95830-48.5894760.5112441.724329e-028.199283e-027.909718e-190.9007647.909718e-191.2101.02.0133.65.2083.66
5Panasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.84.6276185.0SurpriseSurprisingly wonderful little radio Just what we wanted2020-04-100034.951089-65.2816530.7461424.479609e-194.479609e-191.060368e-020.9893964.479609e-194.9175.05.080.48.2029.52
6Panasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.84.6276184.0Fair to good receptionVery good portable radio Great size Fair to good reception in a difficult reception areaI have tried more expensive radios that didpoorly2020-05-230034.951046-29.1126281.9127621.913648e-017.258351e-011.743831e-190.0760456.754892e-034.3664.04.0220.617.8949.15
7Panasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.84.6276184.0Cute LITTLE thing For local stations onlyWorks great for background music and local news in home officeProsAC electric cord or 4 AA batteriesVERY portableits small See photosVery simple to useEasy to read numbersThrowback classic radio lookSound from the one speaker is clear if the station comes inConsPulls in local stations very local but not half as many as your car radio those are built to a very robust standard If your stations are 25 miles away, you probably wont get them unless they have a powerful transmitter or youre in a very good spot for receptionJust basic mono sound but you shouldnt be buying this for sound quality anywayYou might expect something bigger for 292021-03-130134.95752-9.5190010.1232559.594204e-021.900608e-011.275434e-010.3947771.916768e-013.9344.04.01090.647.27-22.25
8Panasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.84.6276185.0good purchaseI really like this radio2021-12-110034.95479-48.4911110.6821043.204450e-191.666134e-026.525723e-030.9768133.204450e-194.7435.04.050.42.0066.40
9Panasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.84.6276185.0Exactly as picturedDoesnt use much power, perfect for camping, brings in lots of stations2021-05-040034.95700-46.6103950.6551322.959141e-192.959141e-192.811365e-020.5864473.854396e-013.7544.04.0120.44.8076.22
product_nameAverage_ratingNum_of_RatingRatingreview_titleReview_TextPosted_DateHelpfulnesshas_imagespriceDay_elapseddepthbreadthTopic_1Topic_2Topic_3Topic_4Topic_5sentiment_score_continuoussentiment_score_discretesst5_sentiment_scoretext_lengthDeviation of star ratingsFOG IndexFlesch Reading Ease
102455Fintie Case for iPad 9.7 2018 2017 / iPad Air 2 / iPad Air 1 - [Corner Protection] Multi-Angle Viewing Folio Cover w/Pocket, Auto Wake/Sleep for iPad 6th / 5th Generation, Ocean Marble4.6434305.0awesome covering ,love that i can prop it up for versatility of usenothing2020-12-310019.99823-100.0000000.4217662.000000e-012.000000e-012.000000e-012.000000e-012.000000e-011.6091.02.010.40.4036.62
102456Fintie Case for iPad 9.7 2018 2017 / iPad Air 2 / iPad Air 1 - [Corner Protection] Multi-Angle Viewing Folio Cover w/Pocket, Auto Wake/Sleep for iPad 6th / 5th Generation, Ocean Marble4.6434305.0Love itiPad air2022-08-110019.99235-65.5124430.6552391.170797e-181.170797e-184.406475e-029.559353e-011.170797e-183.8365.03.020.40.80120.21
102457Fintie Case for iPad 9.7 2018 2017 / iPad Air 2 / iPad Air 1 - [Corner Protection] Multi-Angle Viewing Folio Cover w/Pocket, Auto Wake/Sleep for iPad 6th / 5th Generation, Ocean Marble4.6434305.0BeautifulI am pleased with this iPad cover It is bulkier than I wanted, but I am overall pleased2020-11-030019.99881-51.1773800.7544118.626995e-046.667965e-039.505683e-199.924693e-019.505683e-194.2544.04.0180.47.2087.05
102458Fintie Case for iPad 9.7 2018 2017 / iPad Air 2 / iPad Air 1 - [Corner Protection] Multi-Angle Viewing Folio Cover w/Pocket, Auto Wake/Sleep for iPad 6th / 5th Generation, Ocean Marble4.6434305.0LoveLove this cover Great quality love the pocket too2023-03-240019.9910-45.6471930.6435984.001619e-017.196841e-021.889583e-195.278697e-011.889583e-194.9165.05.090.48.0479.26
102459Fintie Case for iPad 9.7 2018 2017 / iPad Air 2 / iPad Air 1 - [Corner Protection] Multi-Angle Viewing Folio Cover w/Pocket, Auto Wake/Sleep for iPad 6th / 5th Generation, Ocean Marble4.6434304.0I love itCover for my iPad Love the color2021-08-310019.99580-81.6455940.8039884.421745e-194.421745e-194.421745e-191.000000e+004.421745e-194.6795.04.070.62.8089.75
102460Fintie Case for iPad 9.7 2018 2017 / iPad Air 2 / iPad Air 1 - [Corner Protection] Multi-Angle Viewing Folio Cover w/Pocket, Auto Wake/Sleep for iPad 6th / 5th Generation, Ocean Marble4.6434305.0Great iPad cover at an economical priceIts very sturdy protective Easy to position for free standing2022-01-230019.99435-48.8235773.0847262.333927e-035.245237e-032.007745e-192.007745e-199.924208e-014.8015.04.0100.412.0061.33
102461Fintie Case for iPad 9.7 2018 2017 / iPad Air 2 / iPad Air 1 - [Corner Protection] Multi-Angle Viewing Folio Cover w/Pocket, Auto Wake/Sleep for iPad 6th / 5th Generation, Ocean Marble4.6434305.0Best iPad caseNothing to dislike Excellent quality, easy to use and good price2022-06-170019.99290-26.6638661.5821802.705334e-024.373206e-016.482005e-206.939895e-024.662271e-014.8905.04.0110.411.6768.77
102462Fintie Case for iPad 9.7 2018 2017 / iPad Air 2 / iPad Air 1 - [Corner Protection] Multi-Angle Viewing Folio Cover w/Pocket, Auto Wake/Sleep for iPad 6th / 5th Generation, Ocean Marble4.6434305.0Durable cover for iPadLove this product Color is prettyEasy to hold and prop upUses reading books, playing games, Searching internet2021-03-010019.99763-28.2924490.9341429.884520e-027.816330e-022.179336e-192.685606e-015.544309e-014.8585.04.0170.411.5154.22
102463Fintie Case for iPad 9.7 2018 2017 / iPad Air 2 / iPad Air 1 - [Corner Protection] Multi-Angle Viewing Folio Cover w/Pocket, Auto Wake/Sleep for iPad 6th / 5th Generation, Ocean Marble4.6434305.0Worth itQuick shipping Fits ipad easily Nice colors2021-01-190019.99804-32.8630610.4839442.226626e-022.076929e-036.867447e-199.026192e-017.303760e-024.6305.04.070.48.5164.37
102464Fintie Case for iPad 9.7 2018 2017 / iPad Air 2 / iPad Air 1 - [Corner Protection] Multi-Angle Viewing Folio Cover w/Pocket, Auto Wake/Sleep for iPad 6th / 5th Generation, Ocean Marble4.6434305.0GreatSturdy, easy to hold and maneuver, durable2020-06-250019.991012-30.7538052.7637874.969548e-031.117813e-021.722669e-192.927559e-029.545767e-014.6795.04.070.48.5181.29

Duplicate rows

Most frequently occurring

product_nameAverage_ratingNum_of_RatingRatingreview_titleReview_TextPosted_DateHelpfulnesshas_imagespriceDay_elapseddepthbreadthTopic_1Topic_2Topic_3Topic_4Topic_5sentiment_score_continuoussentiment_score_discretesst5_sentiment_scoretext_lengthDeviation of star ratingsFOG IndexFlesch Reading Ease# duplicates
51byone Amplified HD Digital TV Antenna - Support 4K 1080p and All Older TV's - Indoor Smart Switch Amplifier Signal Booster - Coax HDTV Cable/AC Adapter4.0164535.0Beautiful pictureVery easy to install The picture quality was beautiful Highly recommended2020-01-160017.991197-28.6099452.1055982.087788e-021.054087e-011.421011e-195.559915e-028.181143e-014.8445.05.0111.011.6734.934
36Amazon Basics 3.5mm Aux Jack Audio Extension Cable, Male to Female, Adapter for Headphone or Smartphone, 6 Foot, Black4.6577655.0Good cordWorks great, as described2022-05-09006.99377-62.6397802.3386113.657253e-011.006019e-196.342747e-011.006019e-191.006019e-194.6185.05.040.41.6092.804
42Amazon Basics 3.5mm Aux Jack Audio Extension Cable, Male to Female, Adapter for Headphone or Smartphone, 6 Foot, Black4.6577655.0Works as IntendedChord feels a little thin but overall it gets the job done and the audio quality sounds just as good as plugging it directly into the pc apple earbuds2022-04-11006.99405-49.2859621.1331842.059675e-195.456954e-011.824540e-054.542863e-012.059675e-193.8434.04.0290.414.3658.964
199Cable Clips Management - Nightstand Accessories - Cord Organizer - Desk Cable Management - Wire Holder System - Adhesive Cord Clips - Home, Office, Cubicle, Car - Gift Idea - Black4.3209025.0Easy to useThis product adheres, to any surface type and stays Just peel the back paper of a press into the area you wish yo gave it It can hold different size cables2020-10-06008.97912-46.7797010.3542311.762403e-011.027677e-014.284148e-197.209920e-014.284148e-194.4485.04.0310.712.4065.394
444PROZOR 192KHz Digital to Analog Audio Converter DAC Digital SPDIF Optical to Analog L/R RCA Converter Toslink Optical to 3.5mm Jack Adapter for PS3 HD DVD PS4 Amp Apple TV Home Cinema4.4365375.0Works Perfectly Easy InstallWorked as advertised Couldnt be easier to install2021-10-250013.85553-29.4917721.5516561.916472e-022.753437e-197.108954e-021.885149e-017.212308e-014.0454.04.080.613.2046.444
565Upgraded, Anker Soundcore Boost Bluetooth Speaker with Well-Balanced Sound, BassUp, 12H Playtime, USB-C, IPX7 Waterproof, Wireless Speaker with Customizable EQ via App, Wireless Stereo Pairing4.6286652.0Good sound, terrible battery performanceGood sound for a speaker this size However, the battery life is abysmal After about six months, the batter started dying really quickly At this point, it wont even hold a charge Save your money or get a different brand2021-03-0900101.00889-63.2034890.8652121.980023e-194.320803e-011.980023e-195.679197e-011.980023e-191.8392.02.0402.616.0047.804
573Upgraded, Anker Soundcore Boost Bluetooth Speaker with Well-Balanced Sound, BassUp, 12H Playtime, USB-C, IPX7 Waterproof, Wireless Speaker with Customizable EQ via App, Wireless Stereo Pairing4.6286655.0Nice and loudTakes a while to charge but once charged it lasts for a long time2020-11-1000101.001008-81.8677760.8039887.375232e-197.375232e-197.375232e-191.000000e+007.375232e-193.2123.04.0140.45.60108.034
590amFilm Screen Protector for iPad Mini 5/iPad Mini 4, Tempered Glass, 1 Pack4.6198475.0Customer ServiceI had an issue with the first one received, contacted customer service and they sent a new one no charge I haveput it on my Ipad mini and I love it It was so easy to do2020-02-05006.361284-63.3533591.1088852.362201e-192.362201e-192.362201e-194.483169e-015.516831e-014.7955.04.0370.415.8867.764
620iFixit Pro Tech Toolkit - Electronics, Smartphone, Computer & Tablet Repair Kit4.9173185.0quality productVery good quality, worth the money2022-04-020074.95509-63.0609692.1782301.262107e-198.340242e-011.262107e-191.659758e-011.262107e-194.6105.05.060.19.0773.854
21Amazon Basics 3.5mm Aux Jack Audio Extension Cable, Male to Female, Adapter for Headphone or Smartphone, 6 Foot, Black4.6577651.0No SoundThe cord did not work for my son on his Nintendo Switch I read that it did for other reviewers so thats why I purchased it Well it didnt work because he couldnt hear other players once the headphones were plugged into the cord2021-06-19006.99701-81.4432110.8039882.774668e-192.774668e-192.774668e-191.000000e+002.774668e-191.8852.02.0443.619.4252.203